Performance of a high-frequency (180 kHz) acoustic array for tracking juvenile Pacific salmon in the coastal ocean
Bibliographic record
Abstract
Abstract Background Acoustic telemetry is now a key research tool used to quantify juvenile salmon survival, but transmitter size has limited past studies to larger smolts (> 130 mm fork length). New, smaller, higher-frequency transmitters (“tags”) allow studies on a larger fraction of the smolt size spectrum (> 95 mm); however, detection range and study duration are also reduced, introducing new challenges. The potential cost implications are not trivial. With these new transmitters in mind, we designed, deployed, and tested the performance of a dual-frequency receiver array design in the Discovery Islands region of British Columbia, Canada. We double-tagged 50 juvenile steelhead ( Oncorhynchus mykiss ) with large 69-kHz tags (VEMCO model V9-1H) and small 180-kHz tags (model V4-1H). The more powerful 69-kHz tags were used to determine fish presence in order to estimate the detection efficiency (DE) of the 180-kHz tags. We then compared the standard error of the survival estimate produced from the tracking data using the two tag types which has important implications for array performance and hypothesis testing in the sea. Results Perfect detection of the 69-kHz tags allowed us to determine the DE of the 180-kHz tags. Although the 180-kHz tags began to expire during the study, the estimated DE was acceptable at 76% (SE = 9%) when we include single detections. However, 95% confidence intervals on steelhead survival (64%) were 1.5 × larger for the 180-kHz tags (47–85% vs. 51–77% for 69 kHz) because of the reduced DE. Conclusions The array design performed well; however, single detections of the 180-kHz tags indicates that under slightly different circumstances the DE could have been compromised, emphasizing the need to carefully consider the interaction of animal migration characteristics, study design, and tag programming when designing telemetry arrays. To increase DE and improve the precision of 180 kHz-based survival estimates presented here requires either an increase in receiver density, an increase in tag sample size (and modified transmitter programming), or both. The optimal solution depends on transmitter costs, array infrastructure costs, annual maintenance costs, and array use (i.e., contributors). Importantly, the use of smaller tags reduces potential tag burden effects and allows early marine migration studies to be extended to Pacific salmon populations that have been previously impossible to study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".